I am an On-chain and Off-chain Data Analyst with experience in collecting, analyzing, and interpreting blockchain and non-blockchain data to provide insights and make strategic recommendations for organizations in the crypto and non crypto industry. My skill set includes proficiency in Dune, flipsidecrypto, Covalent, graphql, DuckDB, SQL,Malloy, PostgreSQL, PowerBI, excel, statistics, Python and other programming languages, knowledge of blockchain protocols, data visualization, web scraping and machine learning. I have a track record of using my skills to provide insights on network congestion, gas prices, market trends, customer behavior and other on and off-chain metrics to improve performance. I am always keeping up-to-date with the latest developments in the data analysis and blockchain space and looking for new opportunities.
Polygon completed a hard fork last month in hopes of reducing gas fees, Reorganization(reorg) depths. Let’s dive into the network’s health and performance leading up to and since the hard fork. This dashboard aims to answer the following questions: Has this software upgrade led to lower gas fees as hoped?
Analysis of the impact of USDC depegging crisis on the DeFi ecosystem using flipside warehouse, Sql and excel. One important question that has arisen is which stablecoins were affected by the depeg and how it impacted the market.
The project involved conducting a sentiment analysis of Twitter users' opinions on their interactions with eversend's customer service. The analysis was performed using the Python library snscrape. The main objective of the project was to gauge customer satisfaction levels with eversend's customer service and identify any areas that may require improvement. The sentiment analysis involved extracting tweets from Twitter using snscrape and classifying them into positive, negative, or neutral sentiments using natural language processing techniques. The results of the analysis were then used to draw conclusions and make recommendations for eversend to improve their customer service.
This project aimed to conduct a sentiment analysis of Twitter users' opinions on the major candidates running for the 2023 Nigeria presidential elections. The analysis was performed using popular data analysis libraries such as Pandas, duckdb, and Numpy, as well as Excel. The main goal of the project was to evaluate the public perception of the candidates and identify any patterns or trends in the sentiment expressed. The analysis involved collecting tweets from Twitter and categorizing them into positive, negative, or neutral sentiments using natural language processing techniques. The results were then analyzed using statistical tools to draw insights and recommendations for the candidates to improve their campaigns.
in this project we did an exploratory of the Near protocol Blockchain using Nakamoto Coefficient and GINI to determine how decentralized Near is.
in this project we did an exploratory and predictive analysis of crimes in Chicago using SQl, statistic and excel.
in this project we did an exploratory analysis of sales, departments and staffs using SQl.
in this project we queried a single ETH address which includes the gas price, gas used and the transaction hash.
This project involved conducting a market analysis of IT services in various countries using year-over-year metrics. The analysis was performed using Python and a variety of data analysis libraries such as Pandas, duckdb, and map. The objective of the project was to identify trends in the IT services market, such as revenue growth or market share, and determine which countries were experiencing the most significant growth. The analysis involved gathering data from various sources, such as financial reports and market research studies, and organizing the data into a format that could be easily analyzed using statistical methods. The results of the analysis were then used to create recommendations for companies operating in the IT services market to help them make informed decisions about their operations in different countries.